Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations1275
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory966.3 B

Variable types

Categorical8
Text3
Numeric9
Boolean3

Alerts

CPU_company is highly overall correlated with GPU_companyHigh correlation
CPU_freq is highly overall correlated with Price_eurosHigh correlation
Company is highly overall correlated with OS and 1 other fieldsHigh correlation
GPU_company is highly overall correlated with CPU_companyHigh correlation
Inches is highly overall correlated with WeightHigh correlation
OS is highly overall correlated with Company and 1 other fieldsHigh correlation
Price_euros is highly overall correlated with CPU_freq and 3 other fieldsHigh correlation
PrimaryStorage is highly overall correlated with PrimaryStorageTypeHigh correlation
PrimaryStorageType is highly overall correlated with PrimaryStorageHigh correlation
Ram is highly overall correlated with Price_euros and 2 other fieldsHigh correlation
RetinaDisplay is highly overall correlated with Company and 2 other fieldsHigh correlation
Screen is highly overall correlated with ScreenH and 1 other fieldsHigh correlation
ScreenH is highly overall correlated with Price_euros and 3 other fieldsHigh correlation
ScreenW is highly overall correlated with Price_euros and 4 other fieldsHigh correlation
SecondaryStorage is highly overall correlated with SecondaryStorageTypeHigh correlation
SecondaryStorageType is highly overall correlated with SecondaryStorageHigh correlation
Touchscreen is highly overall correlated with TypeNameHigh correlation
TypeName is highly overall correlated with TouchscreenHigh correlation
Weight is highly overall correlated with InchesHigh correlation
OS is highly imbalanced (64.7%)Imbalance
RetinaDisplay is highly imbalanced (89.8%)Imbalance
CPU_company is highly imbalanced (82.1%)Imbalance
SecondaryStorageType is highly imbalanced (66.2%)Imbalance
SecondaryStorage has 1067 (83.7%) zerosZeros

Reproduction

Analysis started2025-02-09 10:31:38.364101
Analysis finished2025-02-09 10:31:47.099054
Duration8.73 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Company
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size76.3 KiB
Dell
291 
Lenovo
289 
HP
268 
Asus
152 
Acer
101 
Other values (14)
174 

Length

Max length9
Median length8
Mean length4.2109804
Min length2

Characters and Unicode

Total characters5369
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApple
2nd rowApple
3rd rowHP
4th rowApple
5th rowApple

Common Values

ValueCountFrequency (%)
Dell 291
22.8%
Lenovo 289
22.7%
HP 268
21.0%
Asus 152
11.9%
Acer 101
 
7.9%
MSI 54
 
4.2%
Toshiba 48
 
3.8%
Apple 21
 
1.6%
Samsung 9
 
0.7%
Mediacom 7
 
0.5%
Other values (9) 35
 
2.7%

Length

2025-02-09T11:31:47.167669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dell 291
22.8%
lenovo 289
22.7%
hp 268
21.0%
asus 152
11.9%
acer 101
 
7.9%
msi 54
 
4.2%
toshiba 48
 
3.8%
apple 21
 
1.6%
samsung 9
 
0.7%
mediacom 7
 
0.5%
Other values (9) 35
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 725
13.5%
o 659
12.3%
l 606
11.3%
s 370
 
6.9%
n 298
 
5.6%
L 292
 
5.4%
D 291
 
5.4%
v 289
 
5.4%
A 274
 
5.1%
H 270
 
5.0%
Other values (27) 1295
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 725
13.5%
o 659
12.3%
l 606
11.3%
s 370
 
6.9%
n 298
 
5.6%
L 292
 
5.4%
D 291
 
5.4%
v 289
 
5.4%
A 274
 
5.1%
H 270
 
5.0%
Other values (27) 1295
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 725
13.5%
o 659
12.3%
l 606
11.3%
s 370
 
6.9%
n 298
 
5.6%
L 292
 
5.4%
D 291
 
5.4%
v 289
 
5.4%
A 274
 
5.1%
H 270
 
5.0%
Other values (27) 1295
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 725
13.5%
o 659
12.3%
l 606
11.3%
s 370
 
6.9%
n 298
 
5.6%
L 292
 
5.4%
D 291
 
5.4%
v 289
 
5.4%
A 274
 
5.1%
H 270
 
5.0%
Other values (27) 1295
24.1%
Distinct618
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Memory size90.5 KiB
2025-02-09T11:31:47.413498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length42
Mean length15.498039
Min length6

Characters and Unicode

Total characters19760
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique444 ?
Unique (%)34.8%

Sample

1st rowMacBook Pro
2nd rowMacbook Air
3rd row250 G6
4th rowMacBook Pro
5th rowMacBook Pro
ValueCountFrequency (%)
inspiron 135
 
5.3%
ideapad 100
 
3.9%
thinkpad 99
 
3.9%
probook 72
 
2.8%
aspire 61
 
2.4%
elitebook 55
 
2.2%
latitude 52
 
2.0%
pro 42
 
1.6%
13 39
 
1.5%
yoga 39
 
1.5%
Other values (682) 1856
72.8%
2025-02-09T11:31:47.794577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1275
 
6.5%
0 1197
 
6.1%
o 1125
 
5.7%
5 1008
 
5.1%
1 884
 
4.5%
i 680
 
3.4%
e 645
 
3.3%
B 629
 
3.2%
7 620
 
3.1%
- 615
 
3.1%
Other values (59) 11082
56.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1275
 
6.5%
0 1197
 
6.1%
o 1125
 
5.7%
5 1008
 
5.1%
1 884
 
4.5%
i 680
 
3.4%
e 645
 
3.3%
B 629
 
3.2%
7 620
 
3.1%
- 615
 
3.1%
Other values (59) 11082
56.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1275
 
6.5%
0 1197
 
6.1%
o 1125
 
5.7%
5 1008
 
5.1%
1 884
 
4.5%
i 680
 
3.4%
e 645
 
3.3%
B 629
 
3.2%
7 620
 
3.1%
- 615
 
3.1%
Other values (59) 11082
56.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1275
 
6.5%
0 1197
 
6.1%
o 1125
 
5.7%
5 1008
 
5.1%
1 884
 
4.5%
i 680
 
3.4%
e 645
 
3.3%
B 629
 
3.2%
7 620
 
3.1%
- 615
 
3.1%
Other values (59) 11082
56.1%

TypeName
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
Notebook
707 
Gaming
205 
Ultrabook
194 
2 in 1 Convertible
117 
Workstation
 
29

Length

Max length18
Median length8
Mean length8.7984314
Min length6

Characters and Unicode

Total characters11218
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUltrabook
2nd rowUltrabook
3rd rowNotebook
4th rowUltrabook
5th rowUltrabook

Common Values

ValueCountFrequency (%)
Notebook 707
55.5%
Gaming 205
 
16.1%
Ultrabook 194
 
15.2%
2 in 1 Convertible 117
 
9.2%
Workstation 29
 
2.3%
Netbook 23
 
1.8%

Length

2025-02-09T11:31:48.031973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:31:48.134888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
notebook 707
43.5%
gaming 205
 
12.6%
ultrabook 194
 
11.9%
2 117
 
7.2%
in 117
 
7.2%
1 117
 
7.2%
convertible 117
 
7.2%
workstation 29
 
1.8%
netbook 23
 
1.4%

Most occurring characters

ValueCountFrequency (%)
o 2730
24.3%
t 1099
9.8%
b 1041
 
9.3%
e 964
 
8.6%
k 953
 
8.5%
N 730
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 428
 
3.8%
351
 
3.1%
Other values (12) 1986
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2730
24.3%
t 1099
9.8%
b 1041
 
9.3%
e 964
 
8.6%
k 953
 
8.5%
N 730
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 428
 
3.8%
351
 
3.1%
Other values (12) 1986
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2730
24.3%
t 1099
9.8%
b 1041
 
9.3%
e 964
 
8.6%
k 953
 
8.5%
N 730
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 428
 
3.8%
351
 
3.1%
Other values (12) 1986
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2730
24.3%
t 1099
9.8%
b 1041
 
9.3%
e 964
 
8.6%
k 953
 
8.5%
N 730
 
6.5%
i 468
 
4.2%
n 468
 
4.2%
a 428
 
3.8%
351
 
3.1%
Other values (12) 1986
17.7%

Inches
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.022902
Minimum10.1
Maximum18.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:48.242834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile12.5
Q114
median15.6
Q315.6
95-th percentile17.3
Maximum18.4
Range8.3
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.4294698
Coefficient of variation (CV)0.095152711
Kurtosis-0.083968898
Mean15.022902
Median Absolute Deviation (MAD)0
Skewness-0.43862218
Sum19154.2
Variance2.043384
MonotonicityNot monotonic
2025-02-09T11:31:48.336467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15.6 647
50.7%
14 193
 
15.1%
17.3 164
 
12.9%
13.3 160
 
12.5%
12.5 39
 
3.1%
11.6 31
 
2.4%
13.5 6
 
0.5%
12 6
 
0.5%
13.9 6
 
0.5%
12.3 5
 
0.4%
Other values (8) 18
 
1.4%
ValueCountFrequency (%)
10.1 4
 
0.3%
11.3 1
 
0.1%
11.6 31
 
2.4%
12 6
 
0.5%
12.3 5
 
0.4%
12.5 39
 
3.1%
13 2
 
0.2%
13.3 160
12.5%
13.5 6
 
0.5%
13.9 6
 
0.5%
ValueCountFrequency (%)
18.4 1
 
0.1%
17.3 164
 
12.9%
17 1
 
0.1%
15.6 647
50.7%
15.4 4
 
0.3%
15 4
 
0.3%
14.1 1
 
0.1%
14 193
 
15.1%
13.9 6
 
0.5%
13.5 6
 
0.5%

Ram
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4407843
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:48.423347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median8
Q38
95-th percentile16
Maximum64
Range62
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.0978094
Coefficient of variation (CV)0.60394972
Kurtosis15.400373
Mean8.4407843
Median Absolute Deviation (MAD)2
Skewness2.698716
Sum10762
Variance25.98766
MonotonicityNot monotonic
2025-02-09T11:31:48.512696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 613
48.1%
4 367
28.8%
16 198
 
15.5%
6 35
 
2.7%
12 25
 
2.0%
32 17
 
1.3%
2 16
 
1.3%
24 3
 
0.2%
64 1
 
0.1%
ValueCountFrequency (%)
2 16
 
1.3%
4 367
28.8%
6 35
 
2.7%
8 613
48.1%
12 25
 
2.0%
16 198
 
15.5%
24 3
 
0.2%
32 17
 
1.3%
64 1
 
0.1%
ValueCountFrequency (%)
64 1
 
0.1%
32 17
 
1.3%
24 3
 
0.2%
16 198
 
15.5%
12 25
 
2.0%
8 613
48.1%
6 35
 
2.7%
4 367
28.8%
2 16
 
1.3%

OS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size82.8 KiB
Windows 10
1048 
No OS
 
66
Linux
 
58
Windows 7
 
45
Chrome OS
 
27
Other values (4)
 
31

Length

Max length12
Median length10
Mean length9.4015686
Min length5

Characters and Unicode

Total characters11987
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmacOS
2nd rowmacOS
3rd rowNo OS
4th rowmacOS
5th rowmacOS

Common Values

ValueCountFrequency (%)
Windows 10 1048
82.2%
No OS 66
 
5.2%
Linux 58
 
4.5%
Windows 7 45
 
3.5%
Chrome OS 27
 
2.1%
macOS 13
 
1.0%
Mac OS X 8
 
0.6%
Windows 10 S 8
 
0.6%
Android 2
 
0.2%

Length

2025-02-09T11:31:48.622406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:31:48.731254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
windows 1101
44.2%
10 1056
42.4%
os 101
 
4.1%
no 66
 
2.6%
linux 58
 
2.3%
7 45
 
1.8%
chrome 27
 
1.1%
macos 13
 
0.5%
mac 8
 
0.3%
x 8
 
0.3%
Other values (2) 10
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1218
10.2%
o 1196
10.0%
i 1161
9.7%
n 1161
9.7%
d 1105
9.2%
W 1101
9.2%
w 1101
9.2%
s 1101
9.2%
1 1056
8.8%
0 1056
8.8%
Other values (17) 731
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11987
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1218
10.2%
o 1196
10.0%
i 1161
9.7%
n 1161
9.7%
d 1105
9.2%
W 1101
9.2%
w 1101
9.2%
s 1101
9.2%
1 1056
8.8%
0 1056
8.8%
Other values (17) 731
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11987
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1218
10.2%
o 1196
10.0%
i 1161
9.7%
n 1161
9.7%
d 1105
9.2%
W 1101
9.2%
w 1101
9.2%
s 1101
9.2%
1 1056
8.8%
0 1056
8.8%
Other values (17) 731
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11987
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1218
10.2%
o 1196
10.0%
i 1161
9.7%
n 1161
9.7%
d 1105
9.2%
W 1101
9.2%
w 1101
9.2%
s 1101
9.2%
1 1056
8.8%
0 1056
8.8%
Other values (17) 731
6.1%

Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0405255
Minimum0.69
Maximum4.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:48.877880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.69
5-th percentile1.17
Q11.5
median2.04
Q32.31
95-th percentile3.2
Maximum4.7
Range4.01
Interquartile range (IQR)0.81

Descriptive statistics

Standard deviation0.66919598
Coefficient of variation (CV)0.32795276
Kurtosis2.4283724
Mean2.0405255
Median Absolute Deviation (MAD)0.39
Skewness1.1508039
Sum2601.67
Variance0.44782325
MonotonicityNot monotonic
2025-02-09T11:31:49.006205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 120
 
9.4%
2.1 58
 
4.5%
2 45
 
3.5%
2.4 42
 
3.3%
2.5 38
 
3.0%
2.3 37
 
2.9%
2.8 28
 
2.2%
1.86 25
 
2.0%
1.9 24
 
1.9%
1.2 24
 
1.9%
Other values (161) 834
65.4%
ValueCountFrequency (%)
0.69 4
0.3%
0.81 2
 
0.2%
0.91 1
 
0.1%
0.92 6
0.5%
0.97 2
 
0.2%
0.98 2
 
0.2%
0.99 1
 
0.1%
1.05 7
0.5%
1.08 2
 
0.2%
1.09 2
 
0.2%
ValueCountFrequency (%)
4.7 1
 
0.1%
4.6 4
 
0.3%
4.5 1
 
0.1%
4.42 11
0.9%
4.4 1
 
0.1%
4.36 4
 
0.3%
4.33 1
 
0.1%
4.3 4
 
0.3%
4.2 3
 
0.2%
4.14 3
 
0.2%

Price_euros
Real number (ℝ)

HIGH CORRELATION 

Distinct791
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1134.9691
Minimum174
Maximum6099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:49.135933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum174
5-th percentile323.397
Q1609
median989
Q31496.5
95-th percentile2449
Maximum6099
Range5925
Interquartile range (IQR)887.5

Descriptive statistics

Standard deviation700.7525
Coefficient of variation (CV)0.61741992
Kurtosis4.3408162
Mean1134.9691
Median Absolute Deviation (MAD)414
Skewness1.5111467
Sum1447085.6
Variance491054.07
MonotonicityNot monotonic
2025-02-09T11:31:49.256646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1799 14
 
1.1%
1099 14
 
1.1%
1499 12
 
0.9%
1199 11
 
0.9%
499 11
 
0.9%
1299 11
 
0.9%
999 10
 
0.8%
1349 9
 
0.7%
899 9
 
0.7%
1399 9
 
0.7%
Other values (781) 1165
91.4%
ValueCountFrequency (%)
174 1
0.1%
191.9 1
0.1%
196 1
0.1%
199 2
0.2%
202.9 1
0.1%
209 2
0.2%
210.8 1
0.1%
224 1
0.1%
229 2
0.2%
239 1
0.1%
ValueCountFrequency (%)
6099 1
0.1%
5499 1
0.1%
4899 1
0.1%
4389 1
0.1%
3975 1
0.1%
3949.4 1
0.1%
3890 1
0.1%
3659.4 1
0.1%
3588.8 1
0.1%
3499 1
0.1%

Screen
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size80.4 KiB
Full HD
835 
Standard
369 
4K Ultra HD
 
43
Quad HD+
 
28

Length

Max length11
Median length7
Mean length7.4462745
Min length7

Characters and Unicode

Total characters9494
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowFull HD
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Full HD 835
65.5%
Standard 369
28.9%
4K Ultra HD 43
 
3.4%
Quad HD+ 28
 
2.2%

Length

2025-02-09T11:31:49.383192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:31:49.482008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hd 906
40.7%
full 835
37.5%
standard 369
16.6%
4k 43
 
1.9%
ultra 43
 
1.9%
quad 28
 
1.3%

Most occurring characters

ValueCountFrequency (%)
l 1713
18.0%
949
10.0%
H 906
9.5%
D 906
9.5%
u 863
9.1%
F 835
8.8%
a 809
8.5%
d 766
8.1%
t 412
 
4.3%
r 412
 
4.3%
Other values (7) 923
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1713
18.0%
949
10.0%
H 906
9.5%
D 906
9.5%
u 863
9.1%
F 835
8.8%
a 809
8.5%
d 766
8.1%
t 412
 
4.3%
r 412
 
4.3%
Other values (7) 923
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1713
18.0%
949
10.0%
H 906
9.5%
D 906
9.5%
u 863
9.1%
F 835
8.8%
a 809
8.5%
d 766
8.1%
t 412
 
4.3%
r 412
 
4.3%
Other values (7) 923
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1713
18.0%
949
10.0%
H 906
9.5%
D 906
9.5%
u 863
9.1%
F 835
8.8%
a 809
8.5%
d 766
8.1%
t 412
 
4.3%
r 412
 
4.3%
Other values (7) 923
9.7%

ScreenW
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1900.0439
Minimum1366
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:49.566588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1366
5-th percentile1366
Q11920
median1920
Q31920
95-th percentile3200
Maximum3840
Range2474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation493.34619
Coefficient of variation (CV)0.25964989
Kurtosis6.5057995
Mean1900.0439
Median Absolute Deviation (MAD)0
Skewness2.2101369
Sum2422556
Variance243390.46
MonotonicityNot monotonic
2025-02-09T11:31:49.655046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1920 838
65.7%
1366 290
 
22.7%
3840 43
 
3.4%
2560 29
 
2.3%
3200 25
 
2.0%
1600 23
 
1.8%
2304 6
 
0.5%
2256 6
 
0.5%
1440 4
 
0.3%
2880 4
 
0.3%
Other values (3) 7
 
0.5%
ValueCountFrequency (%)
1366 290
 
22.7%
1440 4
 
0.3%
1600 23
 
1.8%
1920 838
65.7%
2160 2
 
0.2%
2256 6
 
0.5%
2304 6
 
0.5%
2400 4
 
0.3%
2560 29
 
2.3%
2736 1
 
0.1%
ValueCountFrequency (%)
3840 43
 
3.4%
3200 25
 
2.0%
2880 4
 
0.3%
2736 1
 
0.1%
2560 29
 
2.3%
2400 4
 
0.3%
2304 6
 
0.5%
2256 6
 
0.5%
2160 2
 
0.2%
1920 838
65.7%

ScreenH
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1073.9043
Minimum768
Maximum2160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:49.736532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile768
Q11080
median1080
Q31080
95-th percentile1800
Maximum2160
Range1392
Interquartile range (IQR)0

Descriptive statistics

Standard deviation283.88394
Coefficient of variation (CV)0.26434752
Kurtosis5.7725167
Mean1073.9043
Median Absolute Deviation (MAD)0
Skewness2.1179491
Sum1369228
Variance80590.091
MonotonicityNot monotonic
2025-02-09T11:31:49.824573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1080 833
65.3%
768 290
 
22.7%
2160 43
 
3.4%
1440 31
 
2.4%
1800 29
 
2.3%
900 27
 
2.1%
1600 10
 
0.8%
1504 6
 
0.5%
1200 5
 
0.4%
1824 1
 
0.1%
ValueCountFrequency (%)
768 290
 
22.7%
900 27
 
2.1%
1080 833
65.3%
1200 5
 
0.4%
1440 31
 
2.4%
1504 6
 
0.5%
1600 10
 
0.8%
1800 29
 
2.3%
1824 1
 
0.1%
2160 43
 
3.4%
ValueCountFrequency (%)
2160 43
 
3.4%
1824 1
 
0.1%
1800 29
 
2.3%
1600 10
 
0.8%
1504 6
 
0.5%
1440 31
 
2.4%
1200 5
 
0.4%
1080 833
65.3%
900 27
 
2.1%
768 290
 
22.7%

Touchscreen
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
1087 
True
188 
ValueCountFrequency (%)
False 1087
85.3%
True 188
 
14.7%
2025-02-09T11:31:49.916294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

IPSpanel
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
918 
True
357 
ValueCountFrequency (%)
False 918
72.0%
True 357
 
28.0%
2025-02-09T11:31:49.991106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

RetinaDisplay
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
1258 
True
 
17
ValueCountFrequency (%)
False 1258
98.7%
True 17
 
1.3%
2025-02-09T11:31:50.065922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

CPU_company
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size77.2 KiB
Intel
1214 
AMD
 
60
Samsung
 
1

Length

Max length7
Median length5
Mean length4.907451
Min length3

Characters and Unicode

Total characters6257
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowIntel
2nd rowIntel
3rd rowIntel
4th rowIntel
5th rowIntel

Common Values

ValueCountFrequency (%)
Intel 1214
95.2%
AMD 60
 
4.7%
Samsung 1
 
0.1%

Length

2025-02-09T11:31:50.165010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:31:50.259750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intel 1214
95.2%
amd 60
 
4.7%
samsung 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 1215
19.4%
I 1214
19.4%
t 1214
19.4%
e 1214
19.4%
l 1214
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1215
19.4%
I 1214
19.4%
t 1214
19.4%
e 1214
19.4%
l 1214
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1215
19.4%
I 1214
19.4%
t 1214
19.4%
e 1214
19.4%
l 1214
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1215
19.4%
I 1214
19.4%
t 1214
19.4%
e 1214
19.4%
l 1214
19.4%
A 60
 
1.0%
M 60
 
1.0%
D 60
 
1.0%
S 1
 
< 0.1%
a 1
 
< 0.1%
Other values (4) 4
 
0.1%

CPU_freq
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3029804
Minimum0.9
Maximum3.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:50.347033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.2
Q12
median2.5
Q32.7
95-th percentile2.8
Maximum3.6
Range2.7
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.50384571
Coefficient of variation (CV)0.21877985
Kurtosis-0.13310693
Mean2.3029804
Median Absolute Deviation (MAD)0.2
Skewness-0.83824575
Sum2936.3
Variance0.2538605
MonotonicityNot monotonic
2025-02-09T11:31:50.453777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.5 285
22.4%
2.8 165
12.9%
2.7 164
12.9%
1.6 124
9.7%
2.3 86
 
6.7%
2 86
 
6.7%
1.8 78
 
6.1%
2.6 74
 
5.8%
1.1 53
 
4.2%
2.4 50
 
3.9%
Other values (15) 110
 
8.6%
ValueCountFrequency (%)
0.9 2
 
0.2%
1 1
 
0.1%
1.1 53
4.2%
1.2 15
 
1.2%
1.3 6
 
0.5%
1.44 12
 
0.9%
1.5 10
 
0.8%
1.6 124
9.7%
1.8 78
6.1%
1.9 2
 
0.2%
ValueCountFrequency (%)
3.6 5
 
0.4%
3.2 1
 
0.1%
3.1 3
 
0.2%
3 19
 
1.5%
2.9 19
 
1.5%
2.8 165
12.9%
2.7 164
12.9%
2.6 74
 
5.8%
2.5 285
22.4%
2.4 50
 
3.9%
Distinct93
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size88.5 KiB
2025-02-09T11:31:50.602897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length14.011765
Min length6

Characters and Unicode

Total characters17865
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)2.7%

Sample

1st rowCore i5
2nd rowCore i5
3rd rowCore i5 7200U
4th rowCore i7
5th rowCore i5
ValueCountFrequency (%)
core 1197
31.2%
i7 515
13.4%
i5 423
 
11.0%
7200u 193
 
5.0%
7700hq 147
 
3.8%
7500u 134
 
3.5%
i3 134
 
3.5%
6006u 81
 
2.1%
celeron 78
 
2.0%
dual 73
 
1.9%
Other values (97) 864
22.5%
2025-02-09T11:31:50.884862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2564
14.4%
0 2152
12.0%
e 1503
8.4%
7 1364
 
7.6%
r 1330
 
7.4%
o 1293
 
7.2%
C 1276
 
7.1%
i 1156
 
6.5%
5 934
 
5.2%
U 790
 
4.4%
Other values (37) 3503
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17865
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2564
14.4%
0 2152
12.0%
e 1503
8.4%
7 1364
 
7.6%
r 1330
 
7.4%
o 1293
 
7.2%
C 1276
 
7.1%
i 1156
 
6.5%
5 934
 
5.2%
U 790
 
4.4%
Other values (37) 3503
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17865
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2564
14.4%
0 2152
12.0%
e 1503
8.4%
7 1364
 
7.6%
r 1330
 
7.4%
o 1293
 
7.2%
C 1276
 
7.1%
i 1156
 
6.5%
5 934
 
5.2%
U 790
 
4.4%
Other values (37) 3503
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17865
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2564
14.4%
0 2152
12.0%
e 1503
8.4%
7 1364
 
7.6%
r 1330
 
7.4%
o 1293
 
7.2%
C 1276
 
7.1%
i 1156
 
6.5%
5 934
 
5.2%
U 790
 
4.4%
Other values (37) 3503
19.6%

PrimaryStorage
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444.51765
Minimum8
Maximum2048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:51.091740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile64
Q1256
median256
Q3512
95-th percentile1024
Maximum2048
Range2040
Interquartile range (IQR)256

Descriptive statistics

Standard deviation365.53773
Coefficient of variation (CV)0.82232444
Kurtosis3.0532188
Mean444.51765
Median Absolute Deviation (MAD)128
Skewness1.5928662
Sum566760
Variance133617.83
MonotonicityNot monotonic
2025-02-09T11:31:51.191199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
256 508
39.8%
1024 240
18.8%
128 175
 
13.7%
512 136
 
10.7%
500 124
 
9.7%
32 43
 
3.4%
2048 16
 
1.3%
64 15
 
1.2%
16 10
 
0.8%
180 5
 
0.4%
Other values (3) 3
 
0.2%
ValueCountFrequency (%)
8 1
 
0.1%
16 10
 
0.8%
32 43
 
3.4%
64 15
 
1.2%
128 175
 
13.7%
180 5
 
0.4%
240 1
 
0.1%
256 508
39.8%
500 124
 
9.7%
508 1
 
0.1%
ValueCountFrequency (%)
2048 16
 
1.3%
1024 240
18.8%
512 136
 
10.7%
508 1
 
0.1%
500 124
 
9.7%
256 508
39.8%
240 1
 
0.1%
180 5
 
0.4%
128 175
 
13.7%
64 15
 
1.2%

SecondaryStorage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.06902
Minimum0
Maximum2048
Zeros1067
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2025-02-09T11:31:51.286569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1024
Maximum2048
Range2048
Interquartile range (IQR)0

Descriptive statistics

Standard deviation415.96066
Coefficient of variation (CV)2.3624863
Kurtosis4.4193571
Mean176.06902
Median Absolute Deviation (MAD)0
Skewness2.2576426
Sum224488
Variance173023.27
MonotonicityNot monotonic
2025-02-09T11:31:51.369016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1067
83.7%
1024 187
 
14.7%
2048 15
 
1.2%
256 3
 
0.2%
500 2
 
0.2%
512 1
 
0.1%
ValueCountFrequency (%)
0 1067
83.7%
256 3
 
0.2%
500 2
 
0.2%
512 1
 
0.1%
1024 187
 
14.7%
2048 15
 
1.2%
ValueCountFrequency (%)
2048 15
 
1.2%
1024 187
 
14.7%
512 1
 
0.1%
500 2
 
0.2%
256 3
 
0.2%
0 1067
83.7%

PrimaryStorageType
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size75.5 KiB
SSD
837 
HDD
359 
Flash Storage
 
71
Hybrid
 
8

Length

Max length13
Median length3
Mean length3.5756863
Min length3

Characters and Unicode

Total characters4559
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSD
2nd rowFlash Storage
3rd rowSSD
4th rowSSD
5th rowSSD

Common Values

ValueCountFrequency (%)
SSD 837
65.6%
HDD 359
28.2%
Flash Storage 71
 
5.6%
Hybrid 8
 
0.6%

Length

2025-02-09T11:31:51.467152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:31:51.553849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ssd 837
62.2%
hdd 359
26.7%
flash 71
 
5.3%
storage 71
 
5.3%
hybrid 8
 
0.6%

Most occurring characters

ValueCountFrequency (%)
S 1745
38.3%
D 1555
34.1%
H 367
 
8.1%
a 142
 
3.1%
r 79
 
1.7%
F 71
 
1.6%
s 71
 
1.6%
h 71
 
1.6%
71
 
1.6%
l 71
 
1.6%
Other values (8) 316
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1745
38.3%
D 1555
34.1%
H 367
 
8.1%
a 142
 
3.1%
r 79
 
1.7%
F 71
 
1.6%
s 71
 
1.6%
h 71
 
1.6%
71
 
1.6%
l 71
 
1.6%
Other values (8) 316
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1745
38.3%
D 1555
34.1%
H 367
 
8.1%
a 142
 
3.1%
r 79
 
1.7%
F 71
 
1.6%
s 71
 
1.6%
h 71
 
1.6%
71
 
1.6%
l 71
 
1.6%
Other values (8) 316
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1745
38.3%
D 1555
34.1%
H 367
 
8.1%
a 142
 
3.1%
r 79
 
1.7%
F 71
 
1.6%
s 71
 
1.6%
h 71
 
1.6%
71
 
1.6%
l 71
 
1.6%
Other values (8) 316
 
6.9%

SecondaryStorageType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
No
1067 
HDD
202 
SSD
 
4
Hybrid
 
2

Length

Max length6
Median length2
Mean length2.1678431
Min length2

Characters and Unicode

Total characters2764
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 1067
83.7%
HDD 202
 
15.8%
SSD 4
 
0.3%
Hybrid 2
 
0.2%

Length

2025-02-09T11:31:51.671794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:31:51.771663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 1067
83.7%
hdd 202
 
15.8%
ssd 4
 
0.3%
hybrid 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 1067
38.6%
o 1067
38.6%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2764
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1067
38.6%
o 1067
38.6%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2764
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1067
38.6%
o 1067
38.6%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2764
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1067
38.6%
o 1067
38.6%
D 408
 
14.8%
H 204
 
7.4%
S 8
 
0.3%
y 2
 
0.1%
b 2
 
0.1%
r 2
 
0.1%
i 2
 
0.1%
d 2
 
0.1%

GPU_company
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size77.4 KiB
Intel
704 
Nvidia
396 
AMD
174 
ARM
 
1

Length

Max length6
Median length5
Mean length5.0360784
Min length3

Characters and Unicode

Total characters6421
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowIntel
2nd rowIntel
3rd rowIntel
4th rowAMD
5th rowIntel

Common Values

ValueCountFrequency (%)
Intel 704
55.2%
Nvidia 396
31.1%
AMD 174
 
13.6%
ARM 1
 
0.1%

Length

2025-02-09T11:31:51.874316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:31:51.978225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intel 704
55.2%
nvidia 396
31.1%
amd 174
 
13.6%
arm 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 792
12.3%
I 704
11.0%
t 704
11.0%
e 704
11.0%
l 704
11.0%
n 704
11.0%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 792
12.3%
I 704
11.0%
t 704
11.0%
e 704
11.0%
l 704
11.0%
n 704
11.0%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 792
12.3%
I 704
11.0%
t 704
11.0%
e 704
11.0%
l 704
11.0%
n 704
11.0%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 792
12.3%
I 704
11.0%
t 704
11.0%
e 704
11.0%
l 704
11.0%
n 704
11.0%
N 396
6.2%
v 396
6.2%
d 396
6.2%
a 396
6.2%
Other values (4) 525
8.2%
Distinct110
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
2025-02-09T11:31:52.140653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length14.666667
Min length9

Characters and Unicode

Total characters18700
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)3.3%

Sample

1st rowIris Plus Graphics 640
2nd rowHD Graphics 6000
3rd rowHD Graphics 620
4th rowRadeon Pro 455
5th rowIris Plus Graphics 650
ValueCountFrequency (%)
graphics 715
19.9%
hd 621
17.3%
geforce 364
10.1%
620 349
9.7%
gtx 234
 
6.5%
520 199
 
5.5%
radeon 167
 
4.6%
1050 94
 
2.6%
uhd 68
 
1.9%
940mx 52
 
1.4%
Other values (91) 732
20.4%
2025-02-09T11:31:52.440531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2334
 
12.5%
0 1472
 
7.9%
G 1321
 
7.1%
r 1138
 
6.1%
c 1079
 
5.8%
a 914
 
4.9%
e 900
 
4.8%
i 766
 
4.1%
s 739
 
4.0%
p 715
 
3.8%
Other values (34) 7322
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2334
 
12.5%
0 1472
 
7.9%
G 1321
 
7.1%
r 1138
 
6.1%
c 1079
 
5.8%
a 914
 
4.9%
e 900
 
4.8%
i 766
 
4.1%
s 739
 
4.0%
p 715
 
3.8%
Other values (34) 7322
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2334
 
12.5%
0 1472
 
7.9%
G 1321
 
7.1%
r 1138
 
6.1%
c 1079
 
5.8%
a 914
 
4.9%
e 900
 
4.8%
i 766
 
4.1%
s 739
 
4.0%
p 715
 
3.8%
Other values (34) 7322
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2334
 
12.5%
0 1472
 
7.9%
G 1321
 
7.1%
r 1138
 
6.1%
c 1079
 
5.8%
a 914
 
4.9%
e 900
 
4.8%
i 766
 
4.1%
s 739
 
4.0%
p 715
 
3.8%
Other values (34) 7322
39.2%

Interactions

2025-02-09T11:31:45.901389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.338081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.104334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.892122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.834072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.631593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.364357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.168180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.092323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.978675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.427503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.188768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.974004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.923826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.707710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.445991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.251893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.174995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:46.063158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.510700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.276666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.063599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.009241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.789701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.538215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.339278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.264743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:46.156346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.600835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.371943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.157903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.102907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.879295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.636410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.432806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.361768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:46.241654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.684869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.457095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.247068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.185966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.963513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.725470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.517256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.455477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:46.324885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.759933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.537670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.330918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.265953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.036344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.807505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.596339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.540339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:46.415448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.848027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.625175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.432876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.357139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.122165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.900674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.685388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.633013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:46.495348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:39.928747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.713890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.536713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.446059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.200597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.992446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.917591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.721473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:46.584449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.024496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:40.811159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:41.639292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:42.546849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:43.289635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:44.087650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.012311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-09T11:31:45.817264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-09T11:31:52.540136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CPU_companyCPU_freqCompanyGPU_companyIPSpanelInchesOSPrice_eurosPrimaryStoragePrimaryStorageTypeRamRetinaDisplayScreenScreenHScreenWSecondaryStorageSecondaryStorageTypeTouchscreenTypeNameWeight
CPU_company1.0000.2250.2390.8090.0910.1220.1260.1550.0790.1090.0000.0000.0690.1890.1370.0000.0000.1070.1080.097
CPU_freq0.2251.0000.2330.3440.2110.2920.1940.5260.1010.3330.4960.2890.2730.2950.3050.3240.2680.2200.3210.330
Company0.2390.2331.0000.3110.3260.2630.5030.2150.1510.2620.1390.8480.2160.3930.4500.2290.2700.2700.3100.223
GPU_company0.8090.3440.3111.0000.1690.3710.1550.2220.1120.1630.2130.0620.2200.2600.2430.3030.2920.2630.4150.387
IPSpanel0.0910.2110.3260.1691.0000.2100.2030.2760.1300.2320.1320.1770.2550.3540.3510.1210.1120.1340.3090.293
Inches0.1220.2920.2630.3710.2101.0000.319-0.0490.2160.3560.1540.2310.251-0.107-0.0970.4190.2660.4210.4620.879
OS0.1260.1940.5030.1550.2030.3191.0000.1150.1810.3710.0000.8500.1820.4100.3380.0340.0580.2210.2460.181
Price_euros0.1550.5260.2150.2220.276-0.0490.1151.000-0.0290.3340.7640.0730.3620.6170.6270.3350.2370.2230.311-0.025
PrimaryStorage0.0790.1010.1510.1120.1300.2160.181-0.0291.0000.5240.0870.0550.202-0.050-0.041-0.3030.1990.0810.1900.186
PrimaryStorageType0.1090.3330.2620.1630.2320.3560.3710.3340.5241.0000.1650.1520.2760.3220.3090.1710.1730.1500.2800.287
Ram0.0000.4960.1390.2130.1320.1540.0000.7640.0870.1651.0000.0000.2040.5360.5460.4060.2600.0870.2220.190
RetinaDisplay0.0000.2890.8480.0620.1770.2310.8500.0730.0550.1520.0001.0000.1760.4850.7060.0000.0170.0270.2480.259
Screen0.0690.2730.2160.2200.2550.2510.1820.3620.2020.2760.2040.1761.0000.9490.9740.1590.1540.3240.2440.192
ScreenH0.1890.2950.3930.2600.354-0.1070.4100.617-0.0500.3220.5360.4850.9491.0000.9960.1850.1470.3970.267-0.101
ScreenW0.1370.3050.4500.2430.351-0.0970.3380.627-0.0410.3090.5460.7060.9740.9961.0000.1890.1430.3940.262-0.092
SecondaryStorage0.0000.3240.2290.3030.1210.4190.0340.335-0.3030.1710.4060.0000.1590.1850.1891.0000.7800.1470.3440.457
SecondaryStorageType0.0000.2680.2700.2920.1120.2660.0580.2370.1990.1730.2600.0170.1540.1470.1430.7801.0000.1440.3880.357
Touchscreen0.1070.2200.2700.2630.1340.4210.2210.2230.0810.1500.0870.0270.3240.3970.3940.1470.1441.0000.7740.359
TypeName0.1080.3210.3100.4150.3090.4620.2460.3110.1900.2800.2220.2480.2440.2670.2620.3440.3880.7741.0000.414
Weight0.0970.3300.2230.3870.2930.8790.181-0.0250.1860.2870.1900.2590.192-0.101-0.0920.4570.3570.3590.4141.000

Missing values

2025-02-09T11:31:46.719991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-09T11:31:46.987953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CompanyProductTypeNameInchesRamOSWeightPrice_eurosScreenScreenWScreenHTouchscreenIPSpanelRetinaDisplayCPU_companyCPU_freqCPU_modelPrimaryStorageSecondaryStoragePrimaryStorageTypeSecondaryStorageTypeGPU_companyGPU_model
0AppleMacBook ProUltrabook13.38macOS1.371339.69Standard25601600NoYesYesIntel2.3Core i51280SSDNoIntelIris Plus Graphics 640
1AppleMacbook AirUltrabook13.38macOS1.34898.94Standard1440900NoNoNoIntel1.8Core i51280Flash StorageNoIntelHD Graphics 6000
2HP250 G6Notebook15.68No OS1.86575.00Full HD19201080NoNoNoIntel2.5Core i5 7200U2560SSDNoIntelHD Graphics 620
3AppleMacBook ProUltrabook15.416macOS1.832537.45Standard28801800NoYesYesIntel2.7Core i75120SSDNoAMDRadeon Pro 455
4AppleMacBook ProUltrabook13.38macOS1.371803.60Standard25601600NoYesYesIntel3.1Core i52560SSDNoIntelIris Plus Graphics 650
5AcerAspire 3Notebook15.64Windows 102.10400.00Standard1366768NoNoNoAMD3.0A9-Series 94205000HDDNoAMDRadeon R5
6AppleMacBook ProUltrabook15.416Mac OS X2.042139.97Standard28801800NoYesYesIntel2.2Core i72560Flash StorageNoIntelIris Pro Graphics
7AppleMacbook AirUltrabook13.38macOS1.341158.70Standard1440900NoNoNoIntel1.8Core i52560Flash StorageNoIntelHD Graphics 6000
8AsusZenBook UX430UNUltrabook14.016Windows 101.301495.00Full HD19201080NoNoNoIntel1.8Core i7 8550U5120SSDNoNvidiaGeForce MX150
9AcerSwift 3Ultrabook14.08Windows 101.60770.00Full HD19201080NoYesNoIntel1.6Core i5 8250U2560SSDNoIntelUHD Graphics 620
CompanyProductTypeNameInchesRamOSWeightPrice_eurosScreenScreenWScreenHTouchscreenIPSpanelRetinaDisplayCPU_companyCPU_freqCPU_modelPrimaryStorageSecondaryStoragePrimaryStorageTypeSecondaryStorageTypeGPU_companyGPU_model
1265LenovoIdeaPad Y700-15ISKNotebook15.68Windows 102.60899.00Full HD19201080NoYesNoIntel2.6Core i7 6700HQ10240HDDNoNvidiaGeForce GTX 960M
1266HPPavilion 15-AW003nvNotebook15.66Windows 102.04549.99Full HD19201080NoNoNoAMD2.9A9-Series 941010240HybridNoAMDRadeon R7 M440
1267DellInspiron 3567Notebook15.68Linux2.30805.99Standard1366768NoNoNoIntel2.7Core i7 7500U10240HDDNoAMDRadeon R5 M430
1268HPStream 11-Y000naNetbook11.62Windows 101.17209.00Standard1366768NoNoNoIntel1.6Celeron Dual Core N3060320Flash StorageNoIntelHD Graphics 400
1269AsusX556UJ-XO044T (i7-6500U/4GB/500GB/GeForceNotebook15.64Windows 102.20720.32Standard1366768NoNoNoIntel2.5Core i7 6500U5000HDDNoNvidiaGeForce 920M
1270LenovoYoga 500-14ISK2 in 1 Convertible14.04Windows 101.80638.00Full HD19201080YesYesNoIntel2.5Core i7 6500U1280SSDNoIntelHD Graphics 520
1271LenovoYoga 900-13ISK2 in 1 Convertible13.316Windows 101.301499.00Quad HD+32001800YesYesNoIntel2.5Core i7 6500U5120SSDNoIntelHD Graphics 520
1272LenovoIdeaPad 100S-14IBRNotebook14.02Windows 101.50229.00Standard1366768NoNoNoIntel1.6Celeron Dual Core N3050640Flash StorageNoIntelHD Graphics
1273HP15-AC110nv (i7-6500U/6GB/1TB/RadeonNotebook15.66Windows 102.19764.00Standard1366768NoNoNoIntel2.5Core i7 6500U10240HDDNoAMDRadeon R5 M330
1274AsusX553SA-XX031T (N3050/4GB/500GB/W10)Notebook15.64Windows 102.20369.00Standard1366768NoNoNoIntel1.6Celeron Dual Core N30505000HDDNoIntelHD Graphics